Real Time Adaptive Behavior:
There is a plethora of Internet and GPS aware mobile devices used to estimate travel routes and Estimated Time of Arrival (ETA). The objective of several embodiments of this invention is to leverage that real-time information to dynamically estimate, to a high level of accuracy, the arrival time of a customer at a destination, such as a store. In one embodiment, the store has been notified of arrival a priori and advance purchase for product/service made. On arrival, said product/service is delivered to customer. The tedium of waiting in line, paying and other time wasting chores is thus avoided.
Managing Customer Satisfaction:
Customer Satisfaction (CS) is monitored and managed by a process control model that attempts just in time delivery e.g. the product is fresh (“Quality”) and the average wait time is low (“Agility”). It does so by estimating (predicting) customer arrival time and current store performance. These two estimates drive the alignment between demand and supply through a process of autonomous negotiations between demand expectations and supply constraints. The alignments are driven by mutual benefit to both parties at multiple levels of the value chain.
The alignment methods taught in this invention are applicable to multiple application domains including: scheduling meeting times for people on the go, providing timed promotions to mobile prospects, based on their ETA near the store, optimizing delivery schedules for packages, shifting schedules to deal with resource constraints, etc.
Scalable Cloud centric Framework
The invention teaches methods salient to providing a comprehensive, scalable, generic yet customizable cloud centric framework, to address proactive routing, dispatching, prioritized scheduling of orders placed in advance, or expected to be placed soon, to a supplier, employing but not limited to:                Software on GPS equipped mobile device preferably with internet access, used to place orders to the system, provide Arrival estimates (ETA) for system self correction.        A generic, scalable, cloud centric scheduler, database and software, which communicates with multiple mobile devices and multiple store locations for multiple service providers.        A customizable prioritized schedule display and input device (e.g. touch screen), at supplier location, which communicates with scheduler and is used to monitor and maintain Customer Satisfaction, based on current store performance and inferred trends.        A generic and extensible framework to support modeling and simulation of viable scheduling alternatives. The methods taught are especially relevant to dealing with bursts/spikes in customer demand and/or where store resources are under provisioned.        
Dynamic Process Control Model
The Process controller and Scheduler relays notifications from store to mobile device, suggests scheduling task start time or just in time order pick up, based on a process control model of the store, its operations, current and historical performance. It requests mobile device updates for estimating accurate arrival times. Frequency distributions of salient events at the store, affecting overall Customer Satisfaction are collected, and analyzed. The entire system is framed as a process control model and salient parameters periodically adjusted to ensure stable performance. Example application indicates web page, and/or mobile app interface which, referring to FIG. 1, performs the following functions:    1. Enables user to select a service provider from a library of categories (see dialog box 160)    2. Choose a preferred service provider from that category listing    3. Select a store location    4. Create/Edit an order, 130, from a store supplied menu dialog (not shown)    5. Place the order (for pick up) by one click 120    6. Customer payment is seamlessly integrated.    7. Saving the order specifics (Store location, item, etc) with a user friendly name, see 160    8. Review order history, 140, including past orders, time of arrival, pick up time, store load characteristics, if provided.    9. Review account settings, 150 which include editable identification information, either photo or security questions,    10. Specify preferred alternate store locations, if default selection is not able to provide timely delivery. This would be an editable entry under account settings, 150    10. Receive Text based messages, if requested, on selected store backlog, either provided by the store or inferred based on current increases/decreases in historical backlog for this time period, day of the week etc, under account settings.    11. Change arrival time, by Editing Order, 130.    12. On arrival, the customer picks up the coffee. Customer Satisfaction feedback is obtained, either from the store or from the customer phone regarding whether the coffee was delivered on time and expected quality. A representative dialog box is shown 170.
When the order is placed, the system scheduler receives request and updates ETA and store displays. After placing order, proactive scheduler ensures that orders are completed, preferably just in time for pick up. Work load variance, changes to estimate time of arrival (ETA) etc is monitored. The data is analyzed to dynamically and proactively change priorities to avoid:    1. Customer arrives “early” due to incorrect ETA and has to wait for delivery    2. Customer arrives per ETA computed, but delivery delayed: task execution time (TET) is in variance with current work load    3. The converse of items 1, 2: the coffee is cold; task started too soon, incorrect ETA and/or TET time estimates.
Intended benefits include:                Reducing time wasted waiting in queues, repeating custom preferences each time an order is placed (e.g. soymilk, no sugar)        Seamless payment activated when order is placed: time to pay eliminated.        Visibility into store backlog, enabling load balancing at the store.        Modeling what-if scenarios to identify bottlenecks, explore alternative delivery options        Inter store collaboration: when stores cannot deliver in time, rerouting to other less busy store locations, convenient to customer, thus maintaining Customer Satisfaction (measured by wait, in this case), despite hiccups in the delivery process.        Intuitive online interfaces so Customer and Stores can monitor and adjust control parameters.        Provide the ability to run simulations so experts (human and software) can tune the system.        
Just in time coffee delivery is chosen as the use case to teach methods broadly applicable to multiple application domains, Components of a “generic” system include:                An aggregate reference signal generator. For example, GPS equipped mobile devices can provide Arrival estimates (ETA). This represents the reference signal in this example. Other reference signals include fuzzy data and external data feeds from other customers/stores etc.        An aggregate control signal generator. This, in response to the reference signals, attempts to “close the loop” and align supply and demand change current behavior. Both quantitative and qualitative data sources are employed.        An adaptive control system that closes the feedback loop, changing its strategies and/or sampling frequencies to minimize the control error (between reference and control systems). The control system must be agile. It may also be back drivable, as in Demand Shaping.        A customizable Alignment Dashboard, which communicates with scheduler and is used to monitor and maintain both demand and supply performance indicators.        A Hierarchical, distributed control system that monitors and tunes overall system behavior. All control systems within it are also designed to support extensibility and scalable performance.        Alignment strategies to support dynamic alignments at multiple levels. The framework also supports simulation and machine learning in both forward and backward control directions.        A collaborative community of such control systems to extend Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Demand Shaping and Customer Relationship Management (CRM) functionality. The control systems are dynamic, distributed (e.g. cloud centric) scalable and autonomously proactive.        